Author

G. Anastassiou

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Open access Jul 2026

Activation-Induced Symmetric Kernels for Neural Network Approximation with Quantitative Error Analysis

This paper studies symmetrized neural network (SNN) operators generated by an adjustable half-hyperbolic tangent activation function. The construction is based on the paired density kernels ℵt and ℵ1/t, whose average defines the symmetric kernel F. This kernel is positive, even, normalized, and preserves the partition...

G. Anastassiou, Seda Karateke, M. Zontul · AppliedMath · 0 citations

Open access Aug 2026

Synthetic Data-Guided Symmetric Neural Network Approximation in Banach Spaces

A Banach space-valued approximation framework based on symmetrized neural network (SNN) operators generated by a deformation-dependent sigmoidal activation function, yielding a positive, even, normalized, and localized density kernel satisfying the partition of unity.

G. Anastassiou, Seda Karateke, M. Zontul · Axioms · 0 citations